Overview

Dataset statistics

Number of variables13
Number of observations195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory104.7 B

Variable types

NUM12
BOOL1

Reproduction

Analysis started2022-01-09 02:01:07.819638
Analysis finished2022-01-09 02:01:38.369209
Duration30.55 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

MDVP:Jitter(Abs) is highly correlated with MDVP:Jitter(%) and 2 other fieldsHigh correlation
MDVP:Jitter(%) is highly correlated with MDVP:Jitter(Abs) and 4 other fieldsHigh correlation
MDVP:RAP is highly correlated with MDVP:Jitter(%) and 4 other fieldsHigh correlation
MDVP:PPQ is highly correlated with MDVP:Jitter(%) and 2 other fieldsHigh correlation
Jitter:DDP is highly correlated with MDVP:Jitter(%) and 4 other fieldsHigh correlation
MDVP:Shimmer(dB) is highly correlated with MDVP:Shimmer and 1 other fieldsHigh correlation
MDVP:Shimmer is highly correlated with MDVP:Shimmer(dB) and 1 other fieldsHigh correlation
MDVP:APQ is highly correlated with MDVP:Shimmer and 1 other fieldsHigh correlation
NHR is highly correlated with MDVP:Jitter(%) and 2 other fieldsHigh correlation
PPE is highly correlated with spread1High correlation
spread1 is highly correlated with PPEHigh correlation
spread1 has unique values Unique
PPE has unique values Unique

Variables

MDVP:Jitter(%)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count173
Unique (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006220461538461538
Minimum0.00168
Maximum0.03316
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:38.487263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00168
5-th percentile0.002211
Q10.00346
median0.00494
Q30.007365
95-th percentile0.015561
Maximum0.03316
Range0.03148
Interquartile range (IQR)0.003905

Descriptive statistics

Standard deviation0.004848133693
Coefficient of variation (CV)0.7793848837
Kurtosis12.03093928
Mean0.006220461538
Median Absolute Deviation (MAD)0.0018
Skewness3.084946201
Sum1.21299
Variance2.35044003e-05
2022-01-09T02:01:38.629114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0036931.5%
 
0.0069431.5%
 
0.0074231.5%
 
0.0078421.0%
 
0.0031421.0%
 
0.0029821.0%
 
0.0047621.0%
 
0.0049421.0%
 
0.0025821.0%
 
0.0054421.0%
 
0.0045921.0%
 
0.0049621.0%
 
0.0045121.0%
 
0.0046221.0%
 
0.0058121.0%
 
0.0050221.0%
 
0.0096821.0%
 
0.0044821.0%
 
0.0034621.0%
 
0.001810.5%
 
0.0083110.5%
 
0.0033110.5%
 
0.003710.5%
 
0.0034210.5%
 
0.005210.5%
 
Other values (148)14875.9%
 
ValueCountFrequency (%) 
0.0016810.5%
 
0.0017410.5%
 
0.0017810.5%
 
0.001810.5%
 
0.0018310.5%
 
0.0018510.5%
 
0.0019810.5%
 
0.0020510.5%
 
0.002110.5%
 
0.0021210.5%
 
ValueCountFrequency (%) 
0.0331610.5%
 
0.0310710.5%
 
0.0301110.5%
 
0.0271410.5%
 
0.0193610.5%
 
0.0187210.5%
 
0.0181310.5%
 
0.0171910.5%
 
0.0162710.5%
 
0.0156810.5%
 

MDVP:Jitter(Abs)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count19
Unique (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395897435897436e-05
Minimum7e-06
Maximum0.00026
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:38.783780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7e-06
5-th percentile1e-05
Q12e-05
median3e-05
Q36e-05
95-th percentile0.0001
Maximum0.00026
Range0.000253
Interquartile range (IQR)4e-05

Descriptive statistics

Standard deviation3.48219086e-05
Coefficient of variation (CV)0.7921456109
Kurtosis10.86904252
Mean4.395897436e-05
Median Absolute Deviation (MAD)1e-05
Skewness2.649071417
Sum0.008572
Variance1.212565319e-09
2022-01-09T02:01:38.927819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3e-054623.6%
 
4e-052814.4%
 
2e-052814.4%
 
1e-052010.3%
 
5e-05178.7%
 
6e-05168.2%
 
8e-0594.6%
 
7e-0584.1%
 
9e-0552.6%
 
9e-0652.6%
 
0.000131.5%
 
0.0001521.0%
 
0.0001121.0%
 
0.0001410.5%
 
0.0001210.5%
 
0.0002210.5%
 
7e-0610.5%
 
0.0002610.5%
 
0.0001610.5%
 
ValueCountFrequency (%) 
7e-0610.5%
 
9e-0652.6%
 
1e-052010.3%
 
2e-052814.4%
 
3e-054623.6%
 
4e-052814.4%
 
5e-05178.7%
 
6e-05168.2%
 
7e-0584.1%
 
8e-0594.6%
 
ValueCountFrequency (%) 
0.0002610.5%
 
0.0002210.5%
 
0.0001610.5%
 
0.0001521.0%
 
0.0001410.5%
 
0.0001210.5%
 
0.0001121.0%
 
0.000131.5%
 
9e-0552.6%
 
8e-0594.6%
 

MDVP:RAP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count155
Unique (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003306410256410257
Minimum0.00068
Maximum0.02144
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:39.084257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00068
5-th percentile0.001118
Q10.00166
median0.0025
Q30.003835
95-th percentile0.008756
Maximum0.02144
Range0.02076
Interquartile range (IQR)0.002175

Descriptive statistics

Standard deviation0.002967774416
Coefficient of variation (CV)0.8975820258
Kurtosis14.21379772
Mean0.003306410256
Median Absolute Deviation (MAD)0.00098
Skewness3.36070845
Sum0.64475
Variance8.807684985e-06
2022-01-09T02:01:39.224307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0016952.6%
 
0.0013431.5%
 
0.0042831.5%
 
0.002531.5%
 
0.0015731.5%
 
0.0016531.5%
 
0.0023221.0%
 
0.0032121.0%
 
0.003721.0%
 
0.0011521.0%
 
0.0036821.0%
 
0.0035221.0%
 
0.0022421.0%
 
0.0011621.0%
 
0.0020521.0%
 
0.002621.0%
 
0.0012721.0%
 
0.0029921.0%
 
0.0031621.0%
 
0.0021421.0%
 
0.0050221.0%
 
0.0026821.0%
 
0.0029121.0%
 
0.0016821.0%
 
0.0033121.0%
 
Other values (130)13770.3%
 
ValueCountFrequency (%) 
0.0006810.5%
 
0.0007510.5%
 
0.0007610.5%
 
0.0009210.5%
 
0.0009310.5%
 
0.0009410.5%
 
0.00110.5%
 
0.0010521.0%
 
0.0010910.5%
 
0.0011310.5%
 
ValueCountFrequency (%) 
0.0214410.5%
 
0.0185410.5%
 
0.01810.5%
 
0.0156810.5%
 
0.0115910.5%
 
0.0111710.5%
 
0.0107510.5%
 
0.0099610.5%
 
0.0091910.5%
 
0.0090510.5%
 

MDVP:PPQ
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count165
Unique (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003446358974358974
Minimum0.00092
Maximum0.01958
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:39.376096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00092
5-th percentile0.001315
Q10.00186
median0.00269
Q30.003955
95-th percentile0.009083
Maximum0.01958
Range0.01866
Interquartile range (IQR)0.002095

Descriptive statistics

Standard deviation0.002758976647
Coefficient of variation (CV)0.8005482503
Kurtosis11.96392212
Mean0.003446358974
Median Absolute Deviation (MAD)0.00094
Skewness3.073892458
Sum0.67204
Variance7.611952139e-06
2022-01-09T02:01:39.527685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0033242.1%
 
0.0028331.5%
 
0.0018231.5%
 
0.0020331.5%
 
0.0032921.0%
 
0.0015521.0%
 
0.0025821.0%
 
0.0028921.0%
 
0.0014821.0%
 
0.0015321.0%
 
0.0018621.0%
 
0.002821.0%
 
0.0019221.0%
 
0.0029221.0%
 
0.0021821.0%
 
0.0018421.0%
 
0.003921.0%
 
0.0033921.0%
 
0.0013521.0%
 
0.00321.0%
 
0.0011321.0%
 
0.0041921.0%
 
0.0016621.0%
 
0.0019421.0%
 
0.0031221.0%
 
Other values (140)14071.8%
 
ValueCountFrequency (%) 
0.0009210.5%
 
0.0009610.5%
 
0.00110.5%
 
0.0010610.5%
 
0.0010710.5%
 
0.0011321.0%
 
0.0011510.5%
 
0.0012210.5%
 
0.0012810.5%
 
0.0013310.5%
 
ValueCountFrequency (%) 
0.0195810.5%
 
0.0169910.5%
 
0.0162810.5%
 
0.0152210.5%
 
0.0115410.5%
 
0.0102710.5%
 
0.009910.5%
 
0.0096310.5%
 
0.0094610.5%
 
0.0090910.5%
 

Jitter:DDP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count180
Unique (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009919948717948717
Minimum0.00204
Maximum0.06433
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:39.693978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00204
5-th percentile0.003354
Q10.004985
median0.00749
Q30.011505
95-th percentile0.026271
Maximum0.06433
Range0.06229
Interquartile range (IQR)0.00652

Descriptive statistics

Standard deviation0.008903344356
Coefficient of variation (CV)0.8975191918
Kurtosis14.22476191
Mean0.009919948718
Median Absolute Deviation (MAD)0.00293
Skewness3.362058448
Sum1.93439
Variance7.926954072e-05
2022-01-09T02:01:39.838808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0050731.5%
 
0.0049621.0%
 
0.0034921.0%
 
0.0099421.0%
 
0.0061621.0%
 
0.0050421.0%
 
0.0040321.0%
 
0.0073121.0%
 
0.0110921.0%
 
0.007521.0%
 
0.0128521.0%
 
0.0069621.0%
 
0.007821.0%
 
0.0087321.0%
 
0.0040210.5%
 
0.0643310.5%
 
0.0258910.5%
 
0.0052810.5%
 
0.00710.5%
 
0.0051910.5%
 
0.0045910.5%
 
0.0124210.5%
 
0.0194110.5%
 
0.0060510.5%
 
0.0040610.5%
 
Other values (155)15579.5%
 
ValueCountFrequency (%) 
0.0020410.5%
 
0.0022510.5%
 
0.0022910.5%
 
0.0027610.5%
 
0.0027810.5%
 
0.0028310.5%
 
0.0030110.5%
 
0.0031410.5%
 
0.0031510.5%
 
0.0032710.5%
 
ValueCountFrequency (%) 
0.0643310.5%
 
0.0556310.5%
 
0.0540110.5%
 
0.0470510.5%
 
0.0347610.5%
 
0.0335110.5%
 
0.0322510.5%
 
0.0298710.5%
 
0.0275610.5%
 
0.0271610.5%
 

MDVP:Shimmer
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count188
Unique (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0297091282051282
Minimum0.00954
Maximum0.11908
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:39.992266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00954
5-th percentile0.011211
Q10.016505
median0.02297
Q30.037885
95-th percentile0.067256
Maximum0.11908
Range0.10954
Interquartile range (IQR)0.02138

Descriptive statistics

Standard deviation0.01885693186
Coefficient of variation (CV)0.6347184518
Kurtosis3.238308111
Mean0.02970912821
Median Absolute Deviation (MAD)0.00839
Skewness1.66648041
Sum5.79328
Variance0.0003555838791
2022-01-09T02:01:40.136600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0172521.0%
 
0.0327321.0%
 
0.0160821.0%
 
0.014521.0%
 
0.0244821.0%
 
0.0229321.0%
 
0.0150321.0%
 
0.0196610.5%
 
0.0214510.5%
 
0.0109810.5%
 
0.0402410.5%
 
0.0323510.5%
 
0.0190610.5%
 
0.0212610.5%
 
0.0229610.5%
 
0.0219910.5%
 
0.0141210.5%
 
0.0413710.5%
 
0.0104310.5%
 
0.0795910.5%
 
0.0218410.5%
 
0.0302610.5%
 
0.0327210.5%
 
0.0275110.5%
 
0.0124210.5%
 
Other values (163)16383.6%
 
ValueCountFrequency (%) 
0.0095410.5%
 
0.0095810.5%
 
0.0101510.5%
 
0.0102210.5%
 
0.0102410.5%
 
0.010310.5%
 
0.0103310.5%
 
0.0104310.5%
 
0.0106410.5%
 
0.0109810.5%
 
ValueCountFrequency (%) 
0.1190810.5%
 
0.0941910.5%
 
0.0917810.5%
 
0.0868410.5%
 
0.0814310.5%
 
0.0795910.5%
 
0.071710.5%
 
0.0711810.5%
 
0.0673410.5%
 
0.0672710.5%
 

MDVP:Shimmer(dB)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count149
Unique (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2822512820512821
Minimum0.085
Maximum1.302
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:40.293579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile0.1018
Q10.1485
median0.221
Q30.35
95-th percentile0.6527
Maximum1.302
Range1.217
Interquartile range (IQR)0.2015

Descriptive statistics

Standard deviation0.1948772901
Coefficient of variation (CV)0.6904389898
Kurtosis5.12819251
Mean0.2822512821
Median Absolute Deviation (MAD)0.086
Skewness1.999388639
Sum55.039
Variance0.03797715818
2022-01-09T02:01:40.430501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.15452.6%
 
0.19742.1%
 
0.12631.5%
 
0.25531.5%
 
0.15531.5%
 
0.14531.5%
 
0.30731.5%
 
0.12931.5%
 
0.2121.0%
 
0.13421.0%
 
0.09721.0%
 
0.11721.0%
 
0.08521.0%
 
0.16421.0%
 
0.19821.0%
 
0.58421.0%
 
0.36421.0%
 
0.51721.0%
 
0.13321.0%
 
0.44121.0%
 
0.16821.0%
 
0.22821.0%
 
0.10721.0%
 
0.13121.0%
 
0.3521.0%
 
Other values (124)13468.7%
 
ValueCountFrequency (%) 
0.08521.0%
 
0.08910.5%
 
0.0910.5%
 
0.09310.5%
 
0.09410.5%
 
0.09721.0%
 
0.09810.5%
 
0.09910.5%
 
0.10310.5%
 
0.10621.0%
 
ValueCountFrequency (%) 
1.30210.5%
 
1.01810.5%
 
0.9310.5%
 
0.89110.5%
 
0.83310.5%
 
0.82110.5%
 
0.78410.5%
 
0.77210.5%
 
0.72210.5%
 
0.65910.5%
 

MDVP:APQ
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count189
Unique (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02408148717948718
Minimum0.00719
Maximum0.13778
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:40.577421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00719
5-th percentile0.009114
Q10.01308
median0.01826
Q30.0294
95-th percentile0.057718
Maximum0.13778
Range0.13059
Interquartile range (IQR)0.01632

Descriptive statistics

Standard deviation0.01694673625
Coefficient of variation (CV)0.7037246546
Kurtosis11.16328838
Mean0.02408148718
Median Absolute Deviation (MAD)0.00636
Skewness2.618046502
Sum4.69589
Variance0.0002871918694
2022-01-09T02:01:40.719466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.011421.0%
 
0.0377221.0%
 
0.0123421.0%
 
0.0090321.0%
 
0.0149121.0%
 
0.0113321.0%
 
0.034310.5%
 
0.0179910.5%
 
0.0245410.5%
 
0.1377810.5%
 
0.011910.5%
 
0.035910.5%
 
0.01410.5%
 
0.0280910.5%
 
0.037810.5%
 
0.0163610.5%
 
0.0274510.5%
 
0.0091510.5%
 
0.0293110.5%
 
0.0187210.5%
 
0.0331610.5%
 
0.0114810.5%
 
0.0309110.5%
 
0.0345510.5%
 
0.0831810.5%
 
Other values (164)16484.1%
 
ValueCountFrequency (%) 
0.0071910.5%
 
0.0072610.5%
 
0.0076210.5%
 
0.0080210.5%
 
0.0081110.5%
 
0.008610.5%
 
0.0087110.5%
 
0.0088210.5%
 
0.0090321.0%
 
0.0091510.5%
 
ValueCountFrequency (%) 
0.1377810.5%
 
0.0880810.5%
 
0.0831810.5%
 
0.0682410.5%
 
0.064610.5%
 
0.0635910.5%
 
0.0625910.5%
 
0.0619610.5%
 
0.0602310.5%
 
0.0578310.5%
 

NHR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count185
Unique (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02484707692307692
Minimum0.00065
Maximum0.31482
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:40.874113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00065
5-th percentile0.002528
Q10.005925
median0.01166
Q30.02564
95-th percentile0.092044
Maximum0.31482
Range0.31417
Interquartile range (IQR)0.019715

Descriptive statistics

Standard deviation0.04041844856
Coefficient of variation (CV)1.626688269
Kurtosis21.99497411
Mean0.02484707692
Median Absolute Deviation (MAD)0.0069
Skewness4.220709129
Sum4.84518
Variance0.001633650984
2022-01-09T02:01:41.022157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0023121.0%
 
0.006221.0%
 
0.0047621.0%
 
0.0104921.0%
 
0.0083921.0%
 
0.0047921.0%
 
0.004221.0%
 
0.0722321.0%
 
0.0068121.0%
 
0.003421.0%
 
0.0270710.5%
 
0.0024310.5%
 
0.0087110.5%
 
0.0185910.5%
 
0.0104810.5%
 
0.010710.5%
 
0.0439810.5%
 
0.0102210.5%
 
0.0291910.5%
 
0.0094710.5%
 
0.0007210.5%
 
0.0011910.5%
 
0.0058610.5%
 
0.0172410.5%
 
0.031610.5%
 
Other values (160)16082.1%
 
ValueCountFrequency (%) 
0.0006510.5%
 
0.0007210.5%
 
0.0011910.5%
 
0.0013510.5%
 
0.0016710.5%
 
0.0023121.0%
 
0.0023310.5%
 
0.0023810.5%
 
0.0024310.5%
 
0.0025710.5%
 
ValueCountFrequency (%) 
0.3148210.5%
 
0.259310.5%
 
0.2171310.5%
 
0.1674410.5%
 
0.1626510.5%
 
0.1184310.5%
 
0.1095210.5%
 
0.1074810.5%
 
0.1071510.5%
 
0.1032310.5%
 

status
Boolean

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1
147
0
48
ValueCountFrequency (%) 
114775.4%
 
04824.6%
 

spread1
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count195
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.684396743589745
Minimum-7.964984
Maximum-2.434031
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:41.187517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-7.964984
5-th percentile-7.306315
Q1-6.450096
median-5.720868
Q3-5.046192
95-th percentile-3.7336141
Maximum-2.434031
Range5.530953
Interquartile range (IQR)1.403904

Descriptive statistics

Standard deviation1.090207764
Coefficient of variation (CV)-0.1917895272
Kurtosis-0.05019918161
Mean-5.684396744
Median Absolute Deviation (MAD)0.71853
Skewness0.432138932
Sum-1108.457365
Variance1.188552968
2022-01-09T02:01:41.327505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-5.18598710.5%
 
-4.64957310.5%
 
-7.07241910.5%
 
-5.13203210.5%
 
-5.66021710.5%
 
-6.83681110.5%
 
-5.7918210.5%
 
-6.00641410.5%
 
-7.304510.5%
 
-5.4400410.5%
 
-7.12092510.5%
 
-6.64737910.5%
 
-7.31423710.5%
 
-6.81608610.5%
 
-3.70054410.5%
 
-6.41149710.5%
 
-4.44151910.5%
 
-7.04050810.5%
 
-5.38912910.5%
 
-4.44317910.5%
 
-6.96632110.5%
 
-3.94907910.5%
 
-4.47675510.5%
 
-5.77596610.5%
 
-6.65047110.5%
 
Other values (170)17087.2%
 
ValueCountFrequency (%) 
-7.96498410.5%
 
-7.77768510.5%
 
-7.69573410.5%
 
-7.68258710.5%
 
-7.51793410.5%
 
-7.49626410.5%
 
-7.348310.5%
 
-7.3195110.5%
 
-7.31423710.5%
 
-7.3105510.5%
 
ValueCountFrequency (%) 
-2.43403110.5%
 
-2.83975610.5%
 
-2.92937910.5%
 
-2.9310710.5%
 
-3.26948710.5%
 
-3.29766810.5%
 
-3.37732510.5%
 
-3.44447810.5%
 
-3.58372210.5%
 
-3.70054410.5%
 

spread2
Real number (ℝ≥0)

Distinct count194
Unique (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22651034871794873
Minimum0.006274
Maximum0.450493
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:41.482937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.006274
5-th percentile0.0888389
Q10.1743505
median0.218885
Q30.279234
95-th percentile0.3731391
Maximum0.450493
Range0.444219
Interquartile range (IQR)0.1048835

Descriptive statistics

Standard deviation0.08340576262
Coefficient of variation (CV)0.3682205387
Kurtosis-0.08302289328
Mean0.2265103487
Median Absolute Deviation (MAD)0.048702
Skewness0.1444304855
Sum44.169518
Variance0.006956521238
2022-01-09T02:01:41.620243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.21027921.0%
 
0.31590310.5%
 
0.25498910.5%
 
0.34307310.5%
 
0.20365310.5%
 
0.23053210.5%
 
0.14335910.5%
 
0.35622410.5%
 
0.20745410.5%
 
0.12930310.5%
 
0.20214610.5%
 
0.43432610.5%
 
0.19791910.5%
 
0.32599610.5%
 
0.18805610.5%
 
0.17331910.5%
 
0.18170110.5%
 
0.30601410.5%
 
0.17518110.5%
 
0.12095610.5%
 
0.13391710.5%
 
0.30321410.5%
 
0.26531510.5%
 
0.21803710.5%
 
0.20555810.5%
 
Other values (169)16986.7%
 
ValueCountFrequency (%) 
0.00627410.5%
 
0.01868910.5%
 
0.05684410.5%
 
0.06341210.5%
 
0.06699410.5%
 
0.07329810.5%
 
0.07820210.5%
 
0.08637210.5%
 
0.08716510.5%
 
0.0878410.5%
 
ValueCountFrequency (%) 
0.45049310.5%
 
0.43432610.5%
 
0.41475810.5%
 
0.39774910.5%
 
0.39674610.5%
 
0.39305610.5%
 
0.39100210.5%
 
0.38929510.5%
 
0.38923210.5%
 
0.37553110.5%
 

PPE
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count195
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20655164102564103
Minimum0.044539
Maximum0.527367
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2022-01-09T02:01:41.763987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.044539
5-th percentile0.0915866
Q10.137451
median0.194052
Q30.25298
95-th percentile0.3695708
Maximum0.527367
Range0.482828
Interquartile range (IQR)0.115529

Descriptive statistics

Standard deviation0.09011932248
Coefficient of variation (CV)0.4363040741
Kurtosis0.5283349473
Mean0.206551641
Median Absolute Deviation (MAD)0.058352
Skewness0.7974910716
Sum40.27757
Variance0.008121492285
2022-01-09T02:01:41.897843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.28465410.5%
 
0.14410510.5%
 
0.0962210.5%
 
0.21175610.5%
 
0.17780710.5%
 
0.36897510.5%
 
0.2497410.5%
 
0.25945110.5%
 
0.11551510.5%
 
0.26130510.5%
 
0.27722710.5%
 
0.1330510.5%
 
0.13055410.5%
 
0.09160410.5%
 
0.14740310.5%
 
0.10851410.5%
 
0.14192910.5%
 
0.12177710.5%
 
0.10530610.5%
 
0.13524210.5%
 
0.2827810.5%
 
0.12887210.5%
 
0.04453910.5%
 
0.20919110.5%
 
0.1855810.5%
 
Other values (170)17087.2%
 
ValueCountFrequency (%) 
0.04453910.5%
 
0.05614110.5%
 
0.0576110.5%
 
0.06850110.5%
 
0.07358110.5%
 
0.07558710.5%
 
0.08556910.5%
 
0.08639810.5%
 
0.0914710.5%
 
0.09154610.5%
 
ValueCountFrequency (%) 
0.52736710.5%
 
0.45753310.5%
 
0.45472110.5%
 
0.44477410.5%
 
0.43078810.5%
 
0.41864610.5%
 
0.41033510.5%
 
0.37848310.5%
 
0.37742910.5%
 
0.37096110.5%
 

Interactions

2022-01-09T02:01:11.962934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:12.136773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:12.308987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:12.476782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:12.662678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:12.858763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.040978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.209532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.386911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.565509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.739786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:13.909124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.076117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.251803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.425471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.587230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.770373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:14.944906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.114407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.284668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.456897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.626951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.796158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:15.958377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.119426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.279836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.446489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.601016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.778162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:16.954629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.111768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.282400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.453712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.616887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.787850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:17.943406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:18.096964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:18.306335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:18.495357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:18.694862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:18.908357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:19.111656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:19.655067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:19.841858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.031514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.221187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.412193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.596026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.778889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:20.956134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:21.135284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:21.307138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:21.494681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:21.675257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:21.849289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.023743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.201153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.380349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.551267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.717269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:22.884579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.052433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.222935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.387429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.584182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.767703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:23.977028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:24.157676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:24.339758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:24.519994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:24.697320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:24.859466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.019703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.187830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.361846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.529904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.722701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:25.896935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.064157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.230094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.403112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.578419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.754216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:26.926060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:27.090067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:27.268343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:27.452319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:27.632382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:27.838888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.020238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.193014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.369022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.551572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.734095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:28.909486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.079778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.249153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.425625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.599328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.766511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:29.953475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:30.132771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:30.304802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:30.491366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:30.668313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:30.845090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.014101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.176525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.341296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.505796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.670952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:31.829223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.007319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.175473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.342256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.509592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.680164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:32.850390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:33.011035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:33.560051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:33.719409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:33.882140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.044363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.198467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.370820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.531898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.694194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:34.881238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.045939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.209282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.369400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.523093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.676765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.838343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:35.998270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.150128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.323542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.487756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.647448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.811156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:36.974852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:37.137792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:37.295244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:37.451563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2022-01-09T02:01:42.071252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T02:01:42.385353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T02:01:42.691215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T02:01:43.005989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-09T02:01:37.793275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-01-09T02:01:38.210629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)MDVP:APQNHRstatusspread1spread2PPE
00.007840.000070.003700.005540.011090.043740.4260.029710.022111-4.8130310.2664820.284654
10.009680.000080.004650.006960.013940.061340.6260.043680.019291-4.0751920.3355900.368674
20.010500.000090.005440.007810.016330.052330.4820.035900.013091-4.4431790.3111730.332634
30.009970.000090.005020.006980.015050.054920.5170.037720.013531-4.1175010.3341470.368975
40.012840.000110.006550.009080.019660.064250.5840.044650.017671-3.7477870.2345130.410335
50.009680.000080.004630.007500.013880.047010.4560.032430.012221-4.2428670.2991110.357775
60.003330.000030.001550.002020.004660.016080.1400.013510.006071-5.6343220.2576820.211756
70.002900.000030.001440.001820.004310.015670.1340.012560.003441-6.1676030.1837210.163755
80.005510.000060.002930.003320.008800.020930.1910.017170.010701-5.4986780.3277690.231571
90.005320.000060.002680.003320.008030.028380.2550.024440.010221-5.0118790.3259960.271362

Last rows

MDVP:Jitter(%)MDVP:Jitter(Abs)MDVP:RAPMDVP:PPQJitter:DDPMDVP:ShimmerMDVP:Shimmer(dB)MDVP:APQNHRstatusspread1spread2PPE
1850.003140.000030.001340.001920.004030.015640.1360.016910.007370-5.5925840.1339170.214346
1860.004960.000040.002540.002630.007620.016600.1540.014910.013970-6.4311190.1533100.120605
1870.002670.000020.001150.001480.003450.013000.1170.011440.006800-6.3590180.1166360.138868
1880.003270.000030.001460.001840.004390.011850.1060.010950.007030-6.7102190.1496940.121777
1890.006940.000030.004120.003960.012350.025740.2550.017580.044410-6.9344740.1598900.112838
1900.004590.000030.002630.002590.007900.040870.4050.027450.027640-6.5385860.1219520.133050
1910.005640.000030.003310.002920.009940.027510.2630.018790.018100-6.1953250.1293030.168895
1920.013600.000080.006240.005640.018730.023080.2560.016670.107150-6.7871970.1584530.131728
1930.007400.000040.003700.003900.011090.022960.2410.015880.072230-6.7445770.2074540.123306
1940.005670.000030.002950.003170.008850.018840.1900.013730.043980-5.7240560.1906670.148569